A nomogram predicting pneumonia after cardiac surgery: a retrospective modeling study.

Background: Postoperative pneumonia (POP) is the most prevalent of all nosocomial infections in patients who underwent cardiac surgery. The aim of this study was to identify independent risk factors for pneumonia after cardiac surgery, from which we constructed a nomogram for prediction.

Methods: The clinical data of patients admitted to the Department of Cardiothoracic Surgery of Nanjing Drum Tower Hospital from October 2020 to September 2021 who underwent cardiac surgery were retrospectively analyzed, and the patients were divided into two groups according to whether they had POP: POP group (n=105) and non-POP group (n=1083). Preoperative, intraoperative, and postoperative indicators were collected and analyzed. Logistic regression was used to identify independent risk factors for POP in patients who underwent cardiac surgery. We constructed a nomogram based on these independent risk factors. Model discrimination was assessed via area under the receiver operating characteristic curve (AUC), and calibration was assessed via calibration plot.

Results: A total of 105 events occurred in the 1188 cases. Age (>55 years) (OR: 1.83, P=0.0225), preoperative malnutrition (OR: 3.71, P<0.0001), diabetes mellitus(OR: 2.33, P=0.0036), CPB time (Cardiopulmonary Bypass Time) > 135 min (OR: 2.80, P<0.0001), moderate to severe ARDS (Acute Respiratory Distress Syndrome )(OR: 1.79, P=0.0148), use of ECMO or IABP or CRRT (ECMO: Extra Corporeal Membrane Oxygenation; IABP: Intra-Aortic Balloon Pump; CRRT: Continuous Renal Replacement Therapy )(OR: 2.60, P=0.0057) and MV( Mechanical Ventilation )> 20 hours (OR: 3.11, P<0.0001) were independent risk factors for POP. Based on those independent risk factors, we constructed a simple nomogram with an AUC of 0.82. Calibration plots showed good agreement between predicted probabilities and actual probabilities.

Conclusion: We constructed a facile nomogram for predicting pneumonia after cardiac surgery with good discrimination and calibration. The model has excellent clinical applicability and can be used to identify and adjust modifiable risk factors to reduce the incidence of POP as well as patient mortality.